Text Mining in Big Data

We know that the most common form of information that is available online and in various social networking sites is in the form of text. Text remains the most common mode of information exchange. Many businesses have to examine, interpret and analyze this text data to understand the patterns or meaning. This process of identifying key information in text data for continuous business growth and improvement is known as Text Mining.

It helps them to make crucial decisions to grow their businesses and online marketing efforts. Various methodologies and tools are used. Text Mining can also be said to be the combination of lexical analysis and data mining. Text mining is a very useful technique in social media big data. Text Mining is also known as text analytics.

Areas of Text Mining

Cybercrime Detection

This process can be very useful in detecting various cyber crimes by analyzing the data incoming from various websites and online servers.

Life Sciences

This can be useful in research areas such as bioinformatics and drug discovery. Today, many biotech companies apply the rules of text mining to get useful visualizations for better results.

Social Media Analysis

Social Media Analytics when performed on text data can help analysts find various correlations and linkages between data points to make better decisions in the future. This information attained after the sentiment analytics can also be used by businesses to reach higher profitability. It can also be used to enhance marketing strategies.

Enforcing Laws and Regulations in a Nation

Text Mining can be used by government intelligence agencies to counter anti-terror activities and resolve crime. When explored further, it can also be developed alongside machine learning to predict the nature of crimes.

The Text Mining Process

The Text Mining process begins as soon as the information to be processed is received. Collection and identification of information are done from that set of received textual data. Information can be received from various data sources as well such as databases, documents, other management systems etc. Processing of this information can be done by various lexical analysis tools to extract various features etc.

Parsing is defined as the process of analyzing a string of symbols on the basis of some grammar rules or simply rules defined beforehand. It is also known as syntactic analysis.

Steps of Text Mining Process

Document Processing is implemented to get the desired results for a particular text document. A successful mining process requires careful processing of the associated text document. The text mining process comprises of various steps:

Keyword Extraction

This step deals with the identification of relevant keywords in the text document. Similar keywords have a better chance of linkage. This keyword linkage provides the base for proper clustering of the text data. These keywords act as nodes in a network.

Classification and Clustering

Various algorithms related to the classification of textual data are applied to classify the text from the source document. Node association also takes place in this step. Clustering is done on the basis of the similarity of the text source. Clustering and classification is directly proportional to the network of data.

Pattern Identification

Also known as Trend Analysis, it is a process in which the nodes in the network are compared to deduce patterns in data.

Some already existing software and tools available today are listed below:

1. R: https://www.rstudio.com/products/rstudio/download/

2. TextAnalyzer; https://www.online-utility.org/text/analyzer.jsp

3. Monarch: https://www.alfasoft.com/en/products/data-analytics/altair/monarch.html

4. IBM LanguageWare Technology

5. Crossminds https://crossminds.ai/knowledge/text-parsing/

6. Attensity: http://www.attensity.com/

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